Participants in a sports match (also referred to as a game, contest, or the like) may have different skill levels that may impact their performance in response to different situations that may arise during the match. Parameters that may impact a sports participant's skill level may include, for example, reaction time, response time, attentiveness, hand-eye coordination, and so forth. These parameters may vary across sports participants, and may also vary for a given sports participant throughout a match depending on the particular situation that the participant encounters. For example, a left-handed baseball player may exhibit a faster reaction and response time when batting against a left-handed pitcher as opposed to a right-handed pitcher. In addition, a sports participant's response to a particular situation may vary over time throughout the match. For example, a tennis player's attentiveness may vary over time causing certain backhand returns to be struck with greater accuracy than other backhand returns.
A sports participant is generally unaware during a sports match of how his/her performance is statistically different for different scenarios that may arise during the match. While a sports participant may be aware of certain macro-level statistics (e.g., number of 2 point field goals made vs. 3 point field goals made during a basketball game), statistical data indicating differences in reaction/response times, attentiveness, etc. for different in-match scenarios is unavailable. Further, while video of a sports match may be viewed and assessed post-match, such analysis fails to elucidate granular differences in performance characteristics over the course of the match for the multitude of match scenarios that may occur. Discussed herein are technical solutions that address at least some of the aforementioned drawbacks as well as other drawbacks.